A spacetime modeling approach for virus transmission based on human activity trajectories: A case study of COVID-19

He trajectory information about patients' behavior expressed in the epidemiological investigation includes space-time location points, as well as the mobile patterns and activities between them. This information can not only trace the source of virus transmission, but also prevent the continued virus transmission. However, this epidemiological investigation information may result in data loss due to patients' memory decline or privacy protection. Therefore, there is a certain degree of uncertainty of the trajectory expressed by epidemiological investigation information when mapped to three-dimensional space-time, including non-uniqueness and incompleteness of space-time location points. This means that it is difficult to describe the space-time behavior of individuals continuously through known trajectory information, and it is necessary to introduce time geography methods that can represent uncertainty. Time Geography can express the space-time uncertainty between precise space-time location points through the space-time prism, so that the space-time range of individuals can be continuously described or enveloped through the prism strings, providing complete space-time information for the visualization of virus transmission. In this study, we use time geography as the calculation basis to construct patient space-volume model and virus space-time volume model by analyzing patient activity trajectories and integrating space-time semantic information related to viral transmission.Through overlay operations on space-time prism chains, the model enables quantitative identification of the spatial scope impacted by viral transmission, providing spatial decision support for backtracking epidemic transmission paths and implementing risk-stratified control measures.